High-resolution separation of bioisomers using ion cloud profiling

Elucidation of complex structures of biomolecules plays a key role in the field of chemistry and life sciences. In the past decade, ion mobility, by coupling with mass spectrometry, has become a unique tool for distinguishing isomers and isoforms of biomolecules. In this study, we develop a concept for performing ion mobility analysis using an ion trap, which enables isomer separation under ultra-high fields to achieve super high resolutions over 10,000. The potential of this technology has been demonstrated for analysis of isomers for biomolecules including disaccharides, phospholipids, and peptides with post-translational modifications.


Performance of the typical ion mobility analysis
The importance of distinguishing bioisomers is well recognized for studies in chemistry and life sciences; the analysis of bioisomers has remained as a challenge even for state-of-the-art ion mobility mass spectrometers ( Supplementary Fig. 1).
In earlier setups, as Synapt G2 (Waters, Wilmslow, U.K.) and tims-TOF (Bruker  4 . With an extended ion separation path up to 1094 m, a higher ion mobility resolution of 1860 was claimed for analyzing Agilent tuning mixture (not isomers) 5 , but has not been shown for analyzing bioisomers.
In this work, ion cloud profiling method operated with a LIT was used to allow high-resolution separations of over 10,000 for analysis of bioisomers ( Supplementary Fig. 1e). The LIT has a simple configuration, which has been widely employed as an ion processing device in modern hybrid mass spectrometers.
In future, the ion cloud profiling technology is expected to facilitate a broad range of applications for biological study.     [8] Single point

Trapped IMS
Mobility and CCS (Ω) Yes 400 [10] Single point Milliseconds to seconds No ion mobility measurement 460 in CV/ΔCV [11] Full spectrum Seconds to minutes

Performance characterization and optimization of the ion cloud profiling method
As a validation of the method, the assignments of each isomeric peaks were performed by identifying characteristic ion fragmentation patter ( Supplementary Fig. 4) or adjusting the concentrations of the samples. For instance, some specific isomers, e.g., trehalose and lactose, m/z 365, have characteristic fragments, m/z 203 for trehalose and m/z 305, for lactose, which could be identified by tandem MS analysis ( Supplementary   Fig. 4). The characteristic fragments of trehalose and lactose were as in the literature 12 .
For other isomers, the peaks could be identified by adjusting the peak intensity in the spectrum and concentrations of the samples. For space charge effect, it was observed that the increase of trapped ion number in LIT II led to a blue shift of VAC to larger values ( Supplementary Fig. 6). Meanwhile, the single peak of trehalose with ion number, PA = 43 mV (top), became split for larger PAs (middle and bottom, Supplementary Fig. 6a), where PA is the peak area of the ions in the spectrum. A similar phenomenon was also observed for the lactose and cellose mixture ( Supplementary Fig. 6b). A possible explanation for the phenomenon was the nonlinear ion motion frequency shift due to the space charge effect 14,16 , which resulted in the simultaneous shift of VAC and peaking spitting. . The lactose and cellose peaks are marked by blue and green, respectively. Space charge effect due to the trapping of an excessive number of ions, e.g., the middle and bottom panels here, leads to performance degradation for structural analysis, such as the VAC shift and peak splitting. In this work, as shown in top, ion number used for ion cloud profiling was optimized to ensure that each isomeric species generates a high-quality single peak in the spectrum, correspondingly.
When the ion number was optimized, performance of ion cloud profiling could be further optimized via tuning working parameters, such as the AC resonance frequency, gas pressure, and Mathieu parameter, q ( Supplementary Fig. 7). As a common knowledge, better resolutions could be achieved with longer time for analysis. A unique feature of the ion trap used here is its infinite time for ion trapping, which could be used for the structural analysis of the isomers. By increasing the analysis time via decreasing the scan speed, a structural resolution For dynamic RF effect, the VAC in each individual measurement might change slightly due to the uncertainty of the initial RF phase for ion analysis ( Supplementary Fig. 9a). This resulted in a normal distribution of VAC of during the measurements, e.g., the lactose (blue) and cellose (green) in Supplementary Fig.   9b. The uncertainty of VAC due to the initial RF could be removed by using an averaged VAC of replicate measurements, as shown in Supplementary Fig. 9c. Here, it should be note that the uncertainty of VAC won't be a problem for mixture analysis because the VAC shift of the mixture in one measurement were synchronized ( Supplementary Fig. 10a). For lactose and cellose mixture, it was observed that their difference of VAC, characterized by parameter d, kept stable in 10 replicate measurements ( Supplementary Fig. 10b). Error bar stands for one standard deviation of the distribution for 10 replicates and centre of error bar is the mean of these replicates. Source data are provided as a Source Data file.

Supplementary Note 4 Theoretical modelling
The model considered here described the ion motion in a LIT 17  The DC and RF components of could be characterized by Mathieu parameters, au and qu: where m is ion mass, e is electron charge carried by the ions, u represents either x or y coordinates.
For ions trapped within the LIT, the equation of ion motion in the electric field was as following: Here, m is ion mass, e is electron charge carried by the ions, and b is damping coefficient of the ions.
For ions subjected to the AC resonance excitation in ion cloud profiling, the equation of ion motion became 2 2 + + ∇ = sin( ) (S4) Here, represents the excitation strength and has = /2 0 . is AC voltage with an angular frequency , is calibration coefficient and has ≈ 0.8 in this work.
To understand the ion cloud profiling theoretically, Equation S4 was further simplified by using pseudopotential well approximation 18

Numerical simulation
Numerical simulation of Equation S4 was also performed by using the fourthorder Runge-Kutta method implemented in a home-made algorithm package, electro-hydrodynamic simulation (EHS) 19,20 . Two simulation ion species of disaccharide isomers, I: m/z = 365 and ′ = 0.0010 (blue) and II: m/z = 365 and ′ = 0.0012 (purple), were used to simulate the ion cloud profiling process under the AC excitation, VAC, of 70 mV ( Fig. 1c and Supplementary Fig. 2b). Here, ′ = 2 / , where is the angular frequence of the RF field, m is ion mass, b is the damping coefficient of the ions. Each isomeric species had 100 simulation ions to simulate the motion behavior of ion cloud. The initial positions and velocities of the ions were sampled stochastically based on a thermal distribution at 300 K ( Supplementary Fig. 2a). Four simulation ion species of disaccharide isomers analyzed in the experiment were used to simulate the ion cloud profiling process as well (Supplementary Fig. 3b). The simulation parameters of the LIT for ion manipulation were the same as disaccharide experiments shown in Supplementary   Tables 2 and 3.